The candidate in this K25 application has a background in theoretical physics and computer science. The long term career goal of this candidate is to combine rigorous training in the field of bioscience with his quantitative and computer skills in order to accelerate the late-stage drug development process for the treatment of tuberculosis (TB). The immediate goal is to develop improved computational methods and tools to determine new optimal combination therapies for TB in the preclinical stage of development. This Career Development Award is requested to support a research and training program that includes intensive coursework, mentored readings, attendance at workshops, meetings and seminars, and a research plan that provides for a well-founded understanding of the host-drug-pathogen interactions for the treatment of TB. The candidate will be supported by the Department of Microbiology, Immunology and Pathology at Colorado State University (CSU). The mentors and collaborators for this proposal include faculty from the Mycobacteria Research Laboratories (MRL), the Department of Environmental &Radiological Health Sciences, Chemical &Biological Engineering, and the Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences. CSU is a leader in infectious disease research, and the MRL, with their 19 faculty and over 100 full-time staff, is the worlds largest group focused on mycobacterial research, providing an ideal environment to be introduced to the field of TB research, and for the success of this project. In order to provide guidance for clinical trial testing of new drug regimens for TB, there is a clear need for an efficient method to determine optimal combination regimens in the preclinical stage of development. While conventional pharmacokinetic/pharmacodynamic (PK/PD) methods have proven useful for the determination of optimal single drug antimicrobial regimens, they are data intensive and have limited utility for a systematic and thorough evaluation of the 3- and 4-drug regimens required to treat TB. The objective and goal of this research plan is therefore to develop and demonstrate the use of a computational framework which provides optimal combination drug dosage regimens for treatment of Mycobacterium tuberculosis infection in mice. The proposed framework is a novel integration of physiologically based mathematical modeling, Bayesian inference, targeted experimental studies, and a rigorous method for dose optimization. The specific aims of this proposal are to: (1) develop the computational framework for host-drug-pathogen interactions and drug dose optimization in mouse TB infection models, and (2) demonstrate the use of the computational framework for the determination of an optimal multidrug regimen in mouse TB infection models. While we seek to establish the feasibility of the proposal using current front-line anti-TB drugs (isoniazid, rifampin, pyrazinamide), the ultimate aim of this project is the application of the methodology to the newer anti-TB drugs in order to render predictions of optimal regimens for testing in clinical trials. PUBLIC HEALTH RELEVANCE STATEMENT: TB is an infectious disease which kills more than 1.6 million people per year, in addition, the emergence of drug-resistant TB strains is threatening a return to a pre-antibiotic era for this disease. This proposal provides a methodology and computational tool to more rapidly develop new combination drug regimens that are needed to confront the risk to public health posed by drug-resistant TB.